@inproceedings{baa29c1a7591449291af4adf3df96d02,
title = "Joint voting prediction for questions and answers in CQA",
abstract = "Community Question Answering (CQA) sites have become valuable repositories that host a massive volume of human knowledge. How can we detect a high-value answer which clears the doubts of many users? Can we tell the user if the question s/he is posting would attract a good answer? In this paper, we aim to answer these questions from the perspective of the voting outcome by the site users. Our key observation is that the voting score of an answer is strongly positively correlated with that of its question, and such correlation could be in turn used to boost the prediction performance. Armed with this observation, we propose a family of algorithms to jointly predict the voting scores of questions and answers soon after they are posted in the CQA sites. Experimental evaluations demonstrate the effectiveness of our approaches.",
author = "Yuan Yao and Hanghang Tong and Tao Xie and Leman Akoglu and Feng Xu and Jian Lu",
note = "Publisher Copyright: {\textcopyright} 2014 IEEE.; 2014 IEEE/ACM International Conference on Advances in Social Networks Analysis and Mining, ASONAM 2014 ; Conference date: 17-08-2014 Through 20-08-2014",
year = "2014",
month = oct,
day = "10",
doi = "10.1109/ASONAM.2014.6921607",
language = "English (US)",
series = "ASONAM 2014 - Proceedings of the 2014 IEEE/ACM International Conference on Advances in Social Networks Analysis and Mining",
publisher = "Institute of Electrical and Electronics Engineers Inc.",
pages = "340--343",
editor = "Xindong Wu and Xindong Wu and Martin Ester and Guandong Xu",
booktitle = "ASONAM 2014 - Proceedings of the 2014 IEEE/ACM International Conference on Advances in Social Networks Analysis and Mining",
address = "United States",
}